When you train a computer vision model detecting multiple objects within an image, you need to define a strategy for computing the loss between ground y_true truth and predicted y_pred sets of bounding boxes. This strategy needs to provide consistent matching between these two sets. The function implemented in this project uses a Hungarian algorithm to determine the optimal assignments between these two sets of bounding boxes and uses it for computing the loss.
Install and update using pip:
~$ pip install hungarian-lossNote, this package does not have extra dependencies except Tensorflow 🎉.
The following example shows how to compute loss for the model head predicting bounding boxes.
from hungarian_loss import hungarian_loss
model = ...
losses = {"...": ..., "bbox": hungarian_loss}
lossWeights = {"...": ..., "bbox": 1}
model.compile(optimizer='adam', loss=losses, loss_weights=lossWeights)Let's assume you are working on a deep learning model detecting multiple objects on an image. For simplicity of this example, let's consider, that our model intends to detect just two objects of kittens (see example below).
Our model predicts 2 bounding boxes where it "thinks" kittens are located. We need to compute the difference between true and predicted bounding boxes to update model weights via back-propagation. But how to know which predicted boxes belong to which true boxes? Without the optimal assignment algorithm which consistently assigns the predicted boxes to the true boxes, we will not be able to successfully train our model.
The loss function implemented in this project can help you. Intuitively you can see that predicted BBox 1 is close to the true BBox 1 and likewise predicted BBox 2 is close to the true BBox 2. the cost of assigning these pairs would be minimal compared to any other combinations. As you can see, this is a classical assignment problem. You can solve this problem using the Hungarian Algorithm. Its Python implementation can be found here. It is also used by DERT Facebook End-to-End Object Detection with Transformers model. However, if you wish to use pure Tensor-based implementation this library is for you.
To give you more insights into this implementation we will review a hypothetical example. Let define true-bounding boxes for objects T1 and T2:
| Object | Bounding boxes |
|---|---|
| T1 | 1., 2., 3., 4. |
| T2 | 5., 6., 7., 8. |
Do the same for the predicted boxes P1 and P2:
| Object | Bounding boxes |
|---|---|
| P1 | 1., 1., 1., 1. |
| P2 | 2., 2., 2., 2. |
et's compute the Euclidean distances between all combinations of True and Predicted bounding boxes:
| P1 | P2 | |
|---|---|---|
| T1 | 3.7416575 | 2.449489 |
| T2 | 11.224972 | 9.273619 |
This algorithm will compute the assignment mask first:
| P1 | P2 | |
|---|---|---|
| T1 | 1 | 0 |
| T2 | 0 | 1 |
And then compute the final error:
loss = (3.7416575 + 9.273619) / 2 = 6.50763825
In contrast, if we would use the different assignment:
loss = (2.449489 + 11.224972) / 2 = 6.8372305
As you can see the error for the optimal assignment is smaller compared to the other solution(s).
For information on how to set up a development environment and how to make a contribution to Hungarian Loss, see the contributing guidelines.
- PyPI Releases: https://pypi.org/project/hungarian-loss/
- Source Code: https://github.com/mmgalushka/hungarian-loss
- Issue Tracker: https://github.com/mmgalushka/hungarian-loss/issues
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